rmarkdown::render(‘./3_Clustering/3_Clustering.Rmd’)

Changes in myeloid and kidney cells after CLP - Analysis of 2 x 10X scRNA-seq samples from 2 pools of WT mice (3 Sham + 3 CLP): comparison of gene expression in different cell populations

indir <- "./processedData/2_1_Resolution_choice"
outdir <- "./processedData/3_Clustering"
dir.create(outdir, recursive = T)
library(Seurat)
integrated <- readRDS(paste0(indir, "/8.integrated.rds"))
integrated
## An object of class Seurat 
## 24399 features across 18055 samples within 2 assays 
## Active assay: integrated (2000 features, 2000 variable features)
##  1 other assay present: RNA
##  2 dimensional reductions calculated: pca, umap
Idents(integrated) <- "integrated_snn_res.1.7"
table(integrated@active.ident)
## 
##    0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
## 1310 1139 1102 1017 1006  914  831  811  763  715  698  642  528  504  456  446 
##   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31 
##  431  425  417  352  348  346  334  327  238  207  204  192  184  144  142  133 
##   32   33   34   35   36   37   38   39 
##  128  101   92   90   88   88   81   81
pal <- colorRampPalette(c("#12999E", "#FAEB09", "#E82564", "#03539C"))
levels <- levels(integrated$integrated_snn_res.1.7)
colors.clusters <- pal(length(levels))
names(colors.clusters) <- levels
colors.clusters
##         0         1         2         3         4         5         6         7 
## "#12999E" "#239F92" "#35A587" "#47AB7B" "#59B270" "#6BB864" "#7DBE59" "#8EC54D" 
##         8         9        10        11        12        13        14        15 
## "#A0CB42" "#B2D136" "#C4D82B" "#D6DE1F" "#E8E414" "#FAEB09" "#F8DB10" "#F7CC16" 
##        16        17        18        19        20        21        22        23 
## "#F5BD1E" "#F4AE25" "#F39E2C" "#F18F33" "#F08039" "#EE7141" "#ED6148" "#EC524F" 
##        24        25        26        27        28        29        30        31 
## "#EA4356" "#E9345D" "#E82564" "#D62868" "#C42C6C" "#B32F70" "#A13375" "#8F3679" 
##        32        33        34        35        36        37        38        39 
## "#7E3A7D" "#6C3D82" "#5B4186" "#49448A" "#37488F" "#264B93" "#144F97" "#03539C"
slices <- rep(1, length(levels))
pie(slices, col = colors.clusters, labels = names(colors.clusters))

d <- DimPlot(integrated, reduction = "umap", pt.size = 0.2, label = T, 
    label.size = 6, cols = colors.clusters)
d

pdf(paste0(outdir, "/1_DimPlot_umap_clusters_pc50_res1_7.pdf"), 
    width = 10, height = 8)
d
dev.off()
## png 
##   2
colors.samples <- c("#12999E", "#FDA908")
names(colors.samples) <- levels(as.factor(integrated$sample.id))
slices <- rep(1, length(colors.samples))
pie(slices, col = colors.samples, labels = names(colors.samples))

p1 <- DimPlot(integrated, reduction = "umap", group.by = "sample.id", 
    pt.size = 0.2, cols = colors.samples)
p2 <- DimPlot(integrated, reduction = "umap", label = TRUE, pt.size = 0.2, 
    label.size = 6, cols = colors.clusters)
library(cowplot)
plot_grid(p1, p2)

pdf(paste0(outdir, "/2_2DimPlots_umap_samples_clusters_pc50_res1_7.pdf"), 
    width = 18, height = 8)
plot_grid(p1, p2)
dev.off()
## png 
##   2
d <- DimPlot(integrated, reduction = "umap", group.by = "sample.id", 
    split.by = "sample.id", pt.size = 0.2, ncol = 2, cols = colors.samples)
d

pdf(paste0(outdir, "/3_DimPlot_umap_split_by_samples.pdf"), width = 16, 
    height = 9)
d
dev.off()
## png 
##   2
f <- FeaturePlot(integrated, features = c("Nphs2", "Slc5a2", 
    "Clcnka", "Slc12a1", "Ptgs2", "Slc12a3", "Calb1", "Aqp2", 
    "Slc4a1", "Slc26a4", "Slc14a2", "Upk1a", "Cd22", "Adgre1", 
    "Pecam1", "Pdgfrb", "Cd68", "Cd14", "Acta2", "Csf3r", "Cd4"), 
    min.cutoff = "q9", order = T)
f

pdf(paste0(outdir, "/4_FeaturePlot_cellID.pdf"), width = 28, 
    height = 42)
f
dev.off()
## png 
##   2

##Annotation of markers based on cluster markers from Susztak Science paper (Park et al., Science 360, 758–763 (2018) and Kidney International (2019) 95, 787–796; https://doi.org/10.1016/

https://science.sciencemag.org/content/360/6390/758.long https://www.kidney-international.org/article/S0085-2538(18)30912-8/fulltext

#Podocyte markers -> cluster 28

f2 <- FeaturePlot(integrated, features = c("Nphs2", "Podxl"), 
    min.cutoff = "q9")
f2

pdf(paste0(outdir, "/5_FeaturePlot_Podo.pdf"), width = 14, height = 7)
f2
dev.off()
## png 
##   2

#Endothelial markers -> cluster 15

f3 <- FeaturePlot(integrated, features = c("Plat", "Pecam1"), 
    min.cutoff = "q9")
f3

pdf(paste0(outdir, "/6_FeaturePlot_Endo.pdf"), width = 14, height = 7)
f3
dev.off()
## png 
##   2

#PT-S1 markers -> clusters 7,8,9

f4 <- FeaturePlot(integrated, features = c("Slc5a2", "Slc5a12"), 
    min.cutoff = "q9")
f4

pdf(paste0(outdir, "/7_FeaturePlot_PTs1.pdf"), width = 14, height = 7)
f4
dev.off()
## png 
##   2

#PT-S2 markers

f5 <- FeaturePlot(integrated, features = c("Fxyd2", "Hrsp12"), 
    min.cutoff = "q9")
f5

pdf(paste0(outdir, "/8_FeaturePlot_PTs2.pdf"), width = 7, height = 7)
f5
dev.off()
## png 
##   2

#PT-S3 markers -> cluster 5

f6 <- FeaturePlot(integrated, features = c("Atp11a", "Slc13a3"), 
    min.cutoff = "q9")
f6

pdf(paste0(outdir, "/9_FeaturePlot_PTs3.pdf"), width = 14, height = 7)
f6
dev.off()
## png 
##   2

#Loop of Henle -> clusters 11, 13, 18

f7 <- FeaturePlot(integrated, features = c("Slc12a1", "Umod"), 
    min.cutoff = "q9")
f7

pdf(paste0(outdir, "/10_FeaturePlot_LOH.pdf"), width = 14, height = 7)
f7
dev.off()
## png 
##   2

#Distal CT -> cluster 10

f8 <- FeaturePlot(integrated, features = c("Slc12a3", "Pvalb"), 
    min.cutoff = "q9")
f8

pdf(paste0(outdir, "/11_FeaturePlot_DCT.pdf"), width = 14, height = 7)
f8
dev.off()
## png 
##   2

#Conn Tubule -> clusters 6, 20, 21, 29

f21 <- FeaturePlot(integrated, features = c("Calb1"), min.cutoff = "q9")
f21

pdf(paste0(outdir, "/12_FeaturePlot_ConnTub.pdf"), width = 7, 
    height = 7)
f21
dev.off()
## png 
##   2

#CD PC -> cluster 21

f9 <- FeaturePlot(integrated, features = c("Aqp2", "Hsd11b2"), 
    min.cutoff = "q9")
f9

pdf(paste0(outdir, "/13_FeaturePlot_CD-PC.pdf"), width = 14, 
    height = 7)
f9
dev.off()
## png 
##   2

#CD-IC -> clusters 24, 29, 39

f10 <- FeaturePlot(integrated, features = c("Atp6v1g3", "Atp6v0d2"), 
    min.cutoff = "q9")
f10

pdf(paste0(outdir, "/14_FeaturePlot_CD-IC.pdf"), width = 14, 
    height = 7)
f10
dev.off()
## png 
##   2

#CD Trans -> cluster 29

f11 <- FeaturePlot(integrated, features = c("Slc26a4", "Insrr", 
    "Rhbg"), min.cutoff = "q9")
f11

pdf(paste0(outdir, "/15_FeaturePlot_CD-Trans.pdf"), width = 14, 
    height = 14)
f11
dev.off()
## png 
##   2

#Fibroblast

f12 <- FeaturePlot(integrated, features = c("Plac8", "S100a4", 
    "Pdgfrb"), min.cutoff = "q9")
f12

pdf(paste0(outdir, "/16_FeaturePlot_Fib.pdf"), width = 14, height = 14)
f12
dev.off()
## png 
##   2

#Macro -> cluster 22

f13 <- FeaturePlot(integrated, features = c("C1qa", "Cd68", "C1qb"), 
    min.cutoff = "q9")
f13

pdf(paste0(outdir, "/17_FeaturePlot_Macro.pdf"), width = 14, 
    height = 14)
f13
dev.off()
## png 
##   2

#PMN -> cluster 36

f14 <- FeaturePlot(integrated, features = c("S100a8", "Ly6g", 
    "S100a9"), min.cutoff = "q9")
f14

pdf(paste0(outdir, "/18_FeaturePlot_PMN.pdf"), width = 14, height = 14)
f14
dev.off()
## png 
##   2

#B lymph -> cluster 37

f15 <- FeaturePlot(integrated, features = c("Cd79a", "Cd79b", 
    "Cd19"), min.cutoff = "q9")
f15

pdf(paste0(outdir, "/19_FeaturePlot_Blymph.pdf"), width = 14, 
    height = 14)
f15
dev.off()
## png 
##   2

#Tlymph -> cluster 30

f16 <- FeaturePlot(integrated, features = c("Ltb", "Cd4", "Cxcr6"), 
    min.cutoff = "q9")
f16

pdf(paste0(outdir, "/20_FeaturePlot_Tlymph.pdf"), width = 14, 
    height = 14)
f16
dev.off()
## png 
##   2

#NK -> cluster 30

f17 <- FeaturePlot(integrated, features = c("Gzma", "Nkg7"), 
    min.cutoff = "q9")
f17

pdf(paste0(outdir, "/21_FeaturePlot_NK.pdf"), width = 14, height = 7)
f17
dev.off()
## png 
##   2

#Novel1

f18 <- FeaturePlot(integrated, features = c("Slc27a2", "Lrp2", 
    "Cdca3"), min.cutoff = "q9")
f18

pdf(paste0(outdir, "/22_FeaturePlot_Novel1.pdf"), width = 14, 
    height = 14)
f18
dev.off()
## png 
##   2
# library(Seurat)
DefaultAssay(integrated) <- "RNA"
clusters <- levels(integrated@active.ident)
conserved.markers <- data.frame(matrix(ncol = 14))
for (c in clusters) {
    print(c)
    markers.c <- FindConservedMarkers(integrated, ident.1 = c, 
        grouping.var = "sample.id", verbose = T)
    markers.c <- cbind(data.frame(cluster = rep(c, dim(markers.c)[1]), 
        gene = rownames(markers.c)), markers.c)
    write.table(markers.c, file = paste0(outdir, "/23_markers_", 
        c, ".txt"))
    colnames(conserved.markers) <- colnames(markers.c)
    conserved.markers <- rbind(conserved.markers, markers.c)
    head(conserved.markers)
}
## [1] "0"
## [1] "1"
## [1] "2"
## [1] "3"
## [1] "4"
## [1] "5"
## [1] "6"
## [1] "7"
## [1] "8"
## [1] "9"
## [1] "10"
## [1] "11"
## [1] "12"
## [1] "13"
## [1] "14"
## [1] "15"
## [1] "16"
## [1] "17"
## [1] "18"
## [1] "19"
## [1] "20"
## [1] "21"
## [1] "22"
## [1] "23"
## [1] "24"
## [1] "25"
## [1] "26"
## [1] "27"
## [1] "28"
## [1] "29"
## [1] "30"
## [1] "31"
## [1] "32"
## [1] "33"
## [1] "34"
## [1] "35"
## [1] "36"
## [1] "37"
## [1] "38"
## [1] "39"
conserved.markers <- conserved.markers[-1, ]
openxlsx::write.xlsx(conserved.markers, file = paste0(outdir, 
    "/23_conserved.markers.xlsx"))
library(Seurat)
integrated <- RenameIdents(integrated, `5` = "PT-s3", `6` = "ConnTub", 
    `7` = "PT-s1", `8` = "PT-s1", `9` = "PT-s1", `10` = "DCT", 
    `11` = "LOH", `13` = "LOH", `15` = "Endo", `18` = "LOH", 
    `20` = "ConnTub", `22` = "Macro", `24` = "CD-IC", `28` = "Podo", 
    `36` = "PMN", `37` = "B lymph", `39` = "CD-IC")

d2 <- DimPlot(integrated, label = TRUE, label.size = 4)
d2

pdf(paste0(outdir, "/24_Dimplot_newidents.pdf"), width = 13, 
    height = 9)
d2
dev.off()
## png 
##   2
d3 <- DimPlot(integrated, group.by = "sample.id", split.by = "sample.id", 
    pt.size = 0.2, ncol = 2)
d3

pdf(paste0(outdir, "/25_DimPlot_newidents_split_by_samples.pdf"), 
    width = 16, height = 9)
d3
dev.off()
## png 
##   2

Identify cells expressing Il6

DefaultAssay(integrated) <- "RNA"
f19 <- FeaturePlot(integrated, features = "Il6", order = T, label = T, 
    label.size = 3)
f19

pdf(paste0(outdir, "/26_FeaturePlot_Il6.pdf"), width = 11, height = 10)
f19
dev.off()
## png 
##   2
f20 <- FeaturePlot(integrated, features = c("Il6"), split.by = "sample.id", 
    max.cutoff = 3, cols = c("grey", "red"), order = T)
f20

pdf(paste0(outdir, "/27_FeaturePlot_Il6-sham-CLP.pdf"), width = 19, 
    height = 10)
f20
dev.off()
## png 
##   2
library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())
integrated$celltype.stim <- paste(Idents(integrated), integrated$sample.id, 
    sep = "_")
integrated$celltype <- Idents(integrated)
Idents(integrated) <- "celltype"
plots <- VlnPlot(integrated, features = c("Il6"), split.by = "sample.id", 
    group.by = "celltype", pt.size = 0, combine = FALSE)
library(patchwork)
wrap_plots(plots = plots, ncol = 1)

d <- DotPlot(integrated, features = "Il6", group.by = "celltype.stim")
openxlsx::write.xlsx(d$data, paste0(outdir, "/28_IL6_expn_per_celltype_stim.xlsx"))
d

pdf(paste0(outdir, "/29_DotPlot_IL6_celltype_stim.pdf"), width = 6, 
    height = 8)
d
dev.off()
## png 
##   2
cluster33 <- WhichCells(integrated, idents = "33")
# others <- WhichCells(integrated, idents = "33", invert = T)
d <- DimPlot(integrated, label=T, group.by="celltype", cells.highlight= list(cluster33), cols.highlight = c("darkblue"
                                                                                                       # , "darkred"
                                                                                                       ), cols= "grey")
d

pdf(paste0(outdir, "/30_DimPlot_integrated_label_group.by_celltype_cell.highlight_cluster33.pdf"))
d
dev.off()
## png 
##   2
saveRDS(integrated, paste0(outdir, "/31.integrated.rds"))

Session Information

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.8 (Santiago)
## 
## Matrix products: default
## BLAS:   /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/libblas.so.3.8.0
## LAPACK: /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/liblapack.so.3.8.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] patchwork_1.1.1    cowplot_1.1.1      ggplot2_3.3.3      SeuratObject_4.0.0
## [5] Seurat_4.0.0      
## 
## loaded via a namespace (and not attached):
##   [1] nlme_3.1-152         matrixStats_0.58.0   RcppAnnoy_0.0.18    
##   [4] RColorBrewer_1.1-2   httr_1.4.2           sctransform_0.3.2   
##   [7] tools_4.0.3          R6_2.5.0             irlba_2.3.3         
##  [10] rpart_4.1-15         KernSmooth_2.23-18   uwot_0.1.10         
##  [13] mgcv_1.8-33          lazyeval_0.2.2       colorspace_2.0-0    
##  [16] withr_2.4.1          tidyselect_1.1.0     gridExtra_2.3       
##  [19] compiler_4.0.3       formatR_1.7          plotly_4.9.3        
##  [22] labeling_0.4.2       scales_1.1.1         lmtest_0.9-38       
##  [25] spatstat.data_2.0-0  ggridges_0.5.3       pbapply_1.4-3       
##  [28] spatstat_1.64-1      goftest_1.2-2        stringr_1.4.0       
##  [31] digest_0.6.27        spatstat.utils_2.0-0 rmarkdown_2.6       
##  [34] pkgconfig_2.0.3      htmltools_0.5.1.1    parallelly_1.23.0   
##  [37] highr_0.8            fastmap_1.1.0        htmlwidgets_1.5.3   
##  [40] rlang_0.4.10         shiny_1.6.0          farver_2.0.3        
##  [43] generics_0.1.0       zoo_1.8-8            jsonlite_1.7.2      
##  [46] ica_1.0-2            zip_2.1.1            dplyr_1.0.4         
##  [49] magrittr_2.0.1       Matrix_1.3-2         Rcpp_1.0.6          
##  [52] munsell_0.5.0        abind_1.4-5          reticulate_1.18     
##  [55] lifecycle_1.0.0      stringi_1.5.3        yaml_2.2.1          
##  [58] MASS_7.3-53.1        Rtsne_0.15           plyr_1.8.6          
##  [61] grid_4.0.3           parallel_4.0.3       listenv_0.8.0       
##  [64] promises_1.2.0.1     ggrepel_0.9.1        crayon_1.4.1        
##  [67] deldir_0.2-9         miniUI_0.1.1.1       lattice_0.20-41     
##  [70] splines_4.0.3        tensor_1.5           knitr_1.31          
##  [73] pillar_1.4.7         igraph_1.2.6         future.apply_1.7.0  
##  [76] reshape2_1.4.4       codetools_0.2-18     leiden_0.3.7        
##  [79] glue_1.4.2           evaluate_0.14        data.table_1.13.6   
##  [82] vctrs_0.3.6          png_0.1-7            httpuv_1.5.5        
##  [85] gtable_0.3.0         RANN_2.6.1           purrr_0.3.4         
##  [88] polyclip_1.10-0      tidyr_1.1.2          scattermore_0.7     
##  [91] future_1.21.0        openxlsx_4.2.3       xfun_0.20           
##  [94] mime_0.10            xtable_1.8-4         later_1.1.0.1       
##  [97] survival_3.2-7       viridisLite_0.3.0    tibble_3.0.6        
## [100] cluster_2.1.1        globals_0.14.0       fitdistrplus_1.1-3  
## [103] ellipsis_0.3.1       ROCR_1.0-11
writeLines(capture.output(sessionInfo()), "./scripts/3_Clustering/3_Clustering.sessionInfo.txt")